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The University of Chicago Personalized Versioning THE UNIVERSITY OF CHICAGO PERSONALIZED VERSIONING: PRODUCT STRATEGIES CONSTRUCTED FROM EXPERIMENTS ON PANDORA A DISSERTATION SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF CHICAGO BOOTH SCHOOL OF BUSINESS IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY BY ALI GOLI CHICAGO, ILLINOIS AUGUST 2020 Copyright c 2020 by Ali Goli All Rights Reserved This thesis is dedicated to my parents. For their endless love, support, and encouragement. TABLE OF CONTENTS LIST OF FIGURES . v LIST OF TABLES . vii ACKNOWLEDGMENTS . viii ABSTRACT . ix 1 INTRODUCTION . 1 2 CONCEPTUAL MODEL . 9 3 FIELD EXPERIMENTS AT PANDORA MEDIA . 16 4 REDUCED-FORM RESULTS . 22 5 ESTIMATION MODELS . 25 5.1 Demand model . 25 5.2 Intensive margin . 29 5.3 Partial control over realized treatment . 30 6 HETEROGENEOUS TREATMENT EFFECTS . 31 7 OPTIMIZING PROFITS . 37 8 DISTRIBUTION OF WELFARE . 43 9 DISCUSSION AND CONCLUSIONS . 48 REFERENCES . 50 iv LIST OF FIGURES 2.1 Decision regions for different types (θα; θβ) for price vector (p; z), where p and γz are assumed to be larger than c. Types that lie in R1 pick the outside op- tion, types in R2 subscribe for the paid service, and those in R3 choose the ad-supported version. 10 2.2 Decision regions for different types (θα; θβ) for a price-ad load-discriminating monopolist. Serving customers in region R1 is not worthwhile; those in R2 will purchase the paid subscription and the rest will use the ad-supported service. 11 2.3 Perfectly separable condition. The seller is uncertain if the consumer is of type 1 (1) (2) or 2, but offering a menu with (p; z) = (θα ; θβ ) yields profits that are equal to the case where the seller has full information. 12 2.4 Inseparable condition. The uncertainty in the types cannot be perfectly dealt with by using a personalize menu. The optimal menu (p; z) is one of the five red dots and depending upon the types and realization probabilities either one can be optimal. 13 2.5 Optimal ad load as a function of ad sensitivity. (a) In this panel, price sensitivity β = 2 − (0:1)α, the optimal ad load is a decreasing function of ad sensitivity because gains from subscriptions do not outweigh the losses. (b) The price sensi- tivity β = 2−2α, and α varies between 0 and 1. In this case, optimal ad load is a non-monotonic function of ad sensitivity, because users with higher ad sensitivity are less price sensitive, and uplift from subscriptions outweighs the losses. 15 3.1 Realized ad load across different treatment arms . 20 3.2 Realized lift in ad load (ads/hours) in the 6x3 condition relative to control as a function of pre-treatment ad load. Note the lift in ad load could vary drasti- cally across different groups, due to differences in the attractiveness of different segments for advertisers. 21 4.1 The trade-offs involved in optimizing ad load. The highest ad load condition seems to deliver 50% more ads compared to control, the ad revenue grows by about the same magnitude even though user churn nullifies some of it. Plus subscription revenue grows by about 25% as users substitute toward the ad-free version as the number of ads decreases. Note that the impact of the treatment on subscription rate stabilizes fairly quickly post-treatment. The number of hours spent on the platform reacts negatively to ad load. 23 5.1 A schematic view of the split neural network architecture used for demand esti- mation. The purple and green parts of the network are separate. Also note that treatment dummies enter right before the last layer. This restriction imposes structure on the network and forces it to learn the relationship between the out- put and ad load even though the amount of variation explained by the treatment dummies could be very small. Furthermore, the split structure of the network allows it to separately learn features that could explain the treatment effect ηi(x) from other constructs that explain cross-sectional differences, that is θ(x) and γ(x). 29 v 6.1 The histogram of predicted change in ad-supported hours for each treatment arm relative to control in the hold-out sample. The x-axis is limited to be between -2 to 2 for aesthetics purposes. The median treatment effect across each condition is represented by the dashed line, and the solid red line represents zero. 33 6.2 Realized change in ad-supported hours for different predicted treatment quintiles in the hold-out sample. 34 6.3 The histogram of predicted change in subscription propensity for each treatment arm relative to control in the hold-out sample. The x-axis is limited to be between -0.03 to 0.03 for aesthetics purposes. The median treatment effect across each condition is represented by the black dashed line, and the solid red line represents zero. 35 6.4 Realized lift in subscription propensity as a function of predicted treatment effect quintile on the hold-out sample. 36 7.1 Change in subscription profits as a function of number of ads served. Each pink dot represents the performance of a personalized assignment rule. Holding fixed the number of ads served, the personalized assignment strategy dominates uniform ad-load strategies that the firm experimented with. 40 7.2 The effect of personalization throughout time. The top-left panel shows the per- sonalized counterpart of the control condition is delivering approximately the same amount of ads as control, whereas the 6x3 condition is delivering 60% more ads relative to control. The top-right figure shows the personalized counterpart increases the number of subscribers by 10%, and this gain is expected to ma- terialize within three months of implementing this policy. Note the impact on ad-supported hours or all hours within three months of implementation seems to be negligible. 41 8.1 Probability of assignment to lower ad load than control as a function of price sensitivity. Note the total number of ads for this assignment was set to be equal to the control condition, and those assigned to the 3x1 condition are effectively receiving higher quality of service on the ad-supported product. (a) Users from lower-income zip codes tend to be more likely to receive an ad-load \discount." (b) Older users tend to be less likely to receive a discount. The algorithm seems to be adjusting the quality of service for users who have higher willingness to pay to make converting incentive compatible for them. 44 8.2 The impact of ad-load personalization on consumer welfare. The figure compares the percentage change in consumer utility across the control condition and its personalized counterpart. On average, personalizing ad load lowers consumer utility by 2%. 45 8.3 The average percentage change in consumer utility. The impact of the policy across (a) different income levels and (b) users of different age. 47 vi LIST OF TABLES 3.1 Comparing treatment and control groups across some of the pre-treatment fea- tures calculated during the first quarter of 2016. All features except for gender and zip code mean income are normalized such that the mean of control is equal to 100. 17 3.2 Experiment conditions for audio-ad arms. The size of each treatment cell is spec- ified as the percentage of all listeners on the platform. The control cell consists of 1% of the total listeners. Rows and columns correspond to pod frequency and size, respectively. 18 3.3 The realized ad load, capacity, and fill rate across treatment/control conditions. Note that, for example in the 6x3 condition, the realized ad capacity differs from 18 ads per hour, because the times when songs finish do not perfectly align with the times that users become eligible to see an ad. Also note that an X% increase in realized ad capacity does not necessarily translate to an X% increase in realized ad load, because the ad load also depends on the fill rate, and the system is more likely to run out of ads in higher-capacity conditions. 20 4.1 Change in monthly active users, subscription rate, and ad-supported hours rela- tive to the control condition across different treatment arms. 24 vii ACKNOWLEDGMENTS Foremost, I would like to thank my advisors, Pradeep K. Chintagunta and Jean-Pierre Dub´e, for their endless support, patience, and motivation. I would like to also thank my thesis committee members, G¨unter Hitsch and Anita Rao, for their interest and helpful comments. I thank my fellow colleagues and co-authors at Pandora media without whom this project would have not been possible. I also thank my fellow classmates and colleagues for the useful discussions and for all the fun we have had in our journey at the University of Chicago. I also want to thank my friends who supported me throughout my PhD studies. Last but not the least, I would like to acknowledge and thank my parents for the sacrifices they made to ensure that I receive a good education. viii ABSTRACT The role of advertising as an \implicit price" has long been recognized by economists and marketers. However, the impact of personalizing implicit prices on firm profits and consumer welfare has not been studied. We first conduct a set of large-scale field experiments on Pandora by exogenously shifting the ad load, that is the implicit price, for ad-supported users. We then use a state-of-the-art machine-learning model to examine the heterogeneous treatment effects of firm’s interventions on listeners behavior, both in terms of listening hours and in terms of the propensity to subscribe to the ad-free version of the product.
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